Fingerprint Matching Incorporating Ridge Features With Minutiae

Choi, Heeseung; Choi, Kyoungtaek; Kim, Jaihie

doi:10.1109/tifs.2010.2103940

Cited by 75 publications

(35 citation statements)

References 16 publications

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“…9 (d-f). The algorithm performance indices (EER, FAR) of three methods [5], [10], [12] and proposed algorithm are reported in Table 2. From the ROC curves, we could see that the proposed algorithm always obtains the best results, which proves the discriminate ability of proposed hybrid minutiae descriptor.…”

Section: Comparison With Other Algorithmsmentioning

confidence: 99%

“…From the ROC curves, Ratha's fixed-radius minutiae descriptor exhibits the worst performance. This phenomenon can be interpreted that in small area images such as narrow swipe sensor, the limited minutiae number cannot guarantee the stability of local Table 2 Comparison results of proposed algorithm and the algorithms in [5], [12] and [10].…”

Section: Comparison With Other Algorithmsmentioning

confidence: 99%

“…A number of attempts have been made to incorporate auxiliary information with minutiae as a local representation to decrease the ambiguousness. These methods consist of, local minutiae structure based [2]- [8], local orientation based [9], [10] and ridge feature based [11], [12] approaches. Among all of the minutiae representations, orientation based approaches can be considered as state-of-the-art ones and they significantly increase the discrimination power of single minutia.…”

Section: Introductionmentioning

confidence: 99%

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Hybrid Minutiae Descriptor for Narrow Fingerprint Verification

Isshiki

et al. 2017

IEICE Trans. Inf. & Syst.

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SUMMARYNarrow swipe sensor based systems have drawn more and more attention in recent years. However, the size of captured image is significantly smaller than that obtained from the traditional area fingerprint sensor. Under this condition the available minutiae number is also limited. Therefore, only employing minutiae with the standard associated feature can hardly achieve high verification accuracy. To solve this problem, we present a novel Hybrid Minutiae Descriptor (HMD) which consists of two modules. The first one: Minutiae Ridge-Valley Orientation Descriptor captures the orientation information around minutia and also the trace points located at associated ridge and valley. The second one: Gabor Binary Code extracts and codes the image patch around minutiae. The proposed HMD enhances the representation capability of minutiae feature, and can be matched very efficiently. Experiments conducted over public databases and the database captured by the narrow swipe sensor show that this innovative method gives rise to significant improvements in reducing FRR (False Reject Rate) and EER (Equal Error Rate). key words: narrow swipe sensor, fingerprint verification, hybrid minutiae descriptor, Gabor feature

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Section: Comparison With Other Algorithmsmentioning

confidence: 99%

Section: Comparison With Other Algorithmsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Hybrid Minutiae Descriptor for Narrow Fingerprint Verification

Isshiki

et al. 2017

IEICE Trans. Inf. & Syst.

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“…In paper [38] introduces a novel fingerprint matching algorithm using both ridge feature and the usual minutiae feature to increase the recognition performance against non linear deformation in fingerprints [38,40]. Ridge count, ridge type, ridge length, rcd are the elements of the ridge features.…”

Section: Finger Print Authenticationmentioning

confidence: 99%

Review of Hand Feature of Unimodal and Multimodal Biometric System

Shaikh¹,

Kolekar²

2016

IJCA

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In this age of digital impersonation, biometric techniques are being used increasingly for authentication technique to prevent unauthorized access. As only biometrics, the authentication of individuals using biological identities, can offer true proof of identity. The increasing interest of biometrics is related to security, forensics and remote managing. Extensive research has been conducted in this area with different techniques. In this paper, unimodal, multimodal and fusion techniques are reviewed for authentication. General TermsSecurity

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“…1, the GA optimization starts by randomly generating chromosomes H (1) (M (1) ), H (2) (M (2) ), . .…”

Section: Genetic Algorithm (Ga)mentioning

confidence: 99%

Unsupervised Fingerprint Recognition

Tsai

Lin

Tseng

2013

IEICE Trans. Inf. & Syst.

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SUMMARYThis study extends conventional fingerprint recognition from a supervised to an unsupervised framework. Instead of enrolling fingerprints from known persons to identify unknown fingerprints, our aim is to partition a collection of unknown fingerprints into clusters, so that each cluster consists of fingerprints from the same finger and the number of generated clusters equals the number of distinct fingers involved in the collection. Such an unsupervised framework is helpful to handle the situation where a collection of captured fingerprints are not from the enrolled people. The task of fingerprint clustering is formulated as a problem of minimizing the clustering errors characterized by the Rand index. We estimate the Rand index by computing the similarities between fingerprints and then apply a genetic algorithm to minimize the Rand index. Experiments conducted using the FVC2002 database show that the proposed fingerprint clustering method outperforms an intuitive method based on hierarchical agglomerative clustering. The experiments also show that the number of clusters determined by our system is close to the true number of distinct fingers involved in the collection.

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Fingerprint Matching Incorporating Ridge Features With Minutiae

Cited by 75 publications

References 16 publications

Hybrid Minutiae Descriptor for Narrow Fingerprint Verification

Hybrid Minutiae Descriptor for Narrow Fingerprint Verification

Review of Hand Feature of Unimodal and Multimodal Biometric System

Unsupervised Fingerprint Recognition

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